DeepLiftShap

class captum.attr.DeepLiftShap(model, multiply_by_inputs=True)[source]

Extends DeepLift algorithm and approximates SHAP values using Deeplift. For each input sample it computes DeepLift attribution with respect to each baseline and averages resulting attributions. More details about the algorithm can be found here:

https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf

Note that the explanation model:

  1. Assumes that input features are independent of one another

  2. Is linear, meaning that the explanations are modeled through

    the additive composition of feature effects.

Although, it assumes a linear model for each explanation, the overall model across multiple explanations can be complex and non-linear.

Parameters:

  • model (nn.Module) – The reference to PyTorch model instance.

  • multiply_by_inputs (bool, optional) –

    Indicates whether to factor model inputs’ multiplier in the final attribution scores. In the literature this is also known as local vs global attribution. If inputs’ multiplier isn’t factored in then that type of attribution method is also called local attribution. If it is, then that type of attribution method is called global. More detailed can be found here: https://arxiv.org/abs/1711.06104

    In case of DeepLiftShap, if multiply_by_inputs is set to True, final sensitivity scores are being multiplied by (inputs - baselines). This flag applies only if custom_attribution_func is set to None.

attribute(inputs, baselines, target=None, additional_forward_args=None, return_convergence_delta=False, custom_attribution_func=None)[source]
Parameters:

  • inputs (Tensor or tuple[Tensor, ...]) – Input for which attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. If model takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples (aka batch size), and if multiple input tensors are provided, the examples must be aligned appropriately.

  • baselines (Tensor, tuple[Tensor, ...], or Callable) –

    Baselines define reference samples that are compared with the inputs. In order to assign attribution scores DeepLift computes the differences between the inputs/outputs and corresponding references. Baselines can be provided as:

    • a single tensor, if inputs is a single tensor, with the first dimension equal to the number of examples in the baselines’ distribution. The remaining dimensions must match with input tensor’s dimension starting from the second dimension.

    • a tuple of tensors, if inputs is a tuple of tensors, with the first dimension of any tensor inside the tuple equal to the number of examples in the baseline’s distribution. The remaining dimensions must match the dimensions of the corresponding input tensor starting from the second dimension.

    • callable function, optionally takes inputs as an argument and either returns a single tensor or a tuple of those.

    It is recommended that the number of samples in the baselines’ tensors is larger than one.

  • target (int, tuple, Tensor, or list, optional) –

    Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, no target index is necessary. For general 2D outputs, targets can be either:

    • a single integer or a tensor containing a single integer, which is applied to all input examples

    • a list of integers or a 1D tensor, with length matching the number of examples in inputs (dim 0). Each integer is applied as the target for the corresponding example.

    For outputs with > 2 dimensions, targets can be either:

    • A single tuple, which contains #output_dims - 1 elements. This target index is applied to all examples.

    • A list of tuples with length equal to the number of examples in inputs (dim 0), and each tuple containing #output_dims - 1 elements. Each tuple is applied as the target for the corresponding example.

    Default: None

  • additional_forward_args (Any, optional) – If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (non-tuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to model in order, following the arguments in inputs. Note that attributions are not computed with respect to these arguments. Default: None

  • return_convergence_delta (bool, optional) – Indicates whether to return convergence delta or not. If return_convergence_delta is set to True convergence delta will be returned in a tuple following attributions. Default: False

  • custom_attribution_func (Callable, optional) –

    A custom function for computing final attribution scores. This function can take at least one and at most three arguments with the following signature:

    • custom_attribution_func(multipliers)

    • custom_attribution_func(multipliers, inputs)

    • custom_attribution_func(multipliers, inputs, baselines)

    In case this function is not provided we use the default logic defined as: multipliers * (inputs - baselines) It is assumed that all input arguments, multipliers, inputs and baselines are provided in tuples of same length. custom_attribution_func returns a tuple of attribution tensors that have the same length as the inputs. Default: None

Returns:

  • attributions (Tensor or tuple[Tensor, …]):

    Attribution score computed based on DeepLift rescale rule with respect to each input feature. Attributions will always be the same size as the provided inputs, with each value providing the attribution of the corresponding input index. If a single tensor is provided as inputs, a single tensor is returned. If a tuple is provided for inputs, a tuple of corresponding sized tensors is returned.

  • delta (Tensor, returned if return_convergence_delta=True):

    This is computed using the property that the total sum of model(inputs) - model(baselines) must be very close to the total sum of attributions computed based on approximated SHAP values using Deeplift’s rescale rule. Delta is calculated for each example input and baseline pair, meaning that the number of elements in returned delta tensor is equal to the number of examples in input * number of examples in baseline. The deltas are ordered in the first place by input example, followed by the baseline. Note that the logic described for deltas is guaranteed when the default logic for attribution computations is used, meaning that the custom_attribution_func=None, otherwise it is not guaranteed and depends on the specifics of the custom_attribution_func.

Return type:

attributions or 2-element tuple of attributions, delta

Examples:

>>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
>>> # and returns an Nx10 tensor of class probabilities.
>>> net = ImageClassifier()
>>> dl = DeepLiftShap(net)
>>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
>>> # Computes shap values using deeplift for class 3.
>>> attribution = dl.attribute(input, target=3)